CN113096800A - Early warning system - Google Patents
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- CN113096800A CN113096800A CN202110634212.5A CN202110634212A CN113096800A CN 113096800 A CN113096800 A CN 113096800A CN 202110634212 A CN202110634212 A CN 202110634212A CN 113096800 A CN113096800 A CN 113096800A
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- 208000024891 symptom Diseases 0.000 claims abstract description 19
- 208000002193 Pain Diseases 0.000 claims description 21
- 230000036760 body temperature Effects 0.000 claims description 10
- 230000001174 ascending effect Effects 0.000 claims description 7
- 201000010099 disease Diseases 0.000 claims description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims 1
- 238000003745 diagnosis Methods 0.000 description 11
- 238000000034 method Methods 0.000 description 8
- 230000006378 damage Effects 0.000 description 4
- 210000001503 joint Anatomy 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 230000000630 rising effect Effects 0.000 description 3
- 210000000689 upper leg Anatomy 0.000 description 3
- 206010060820 Joint injury Diseases 0.000 description 2
- 208000027418 Wounds and injury Diseases 0.000 description 2
- 238000003759 clinical diagnosis Methods 0.000 description 2
- 230000003111 delayed effect Effects 0.000 description 2
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- 208000014674 injury Diseases 0.000 description 2
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- 238000012545 processing Methods 0.000 description 2
- 208000006820 Arthralgia Diseases 0.000 description 1
- 206010061218 Inflammation Diseases 0.000 description 1
- 206010023203 Joint destruction Diseases 0.000 description 1
- 206010023230 Joint stiffness Diseases 0.000 description 1
- 208000032912 Local swelling Diseases 0.000 description 1
- 206010057178 Osteoarthropathies Diseases 0.000 description 1
- 208000035965 Postoperative Complications Diseases 0.000 description 1
- 206010037660 Pyrexia Diseases 0.000 description 1
- 210000001015 abdomen Anatomy 0.000 description 1
- 230000003187 abdominal effect Effects 0.000 description 1
- 238000002266 amputation Methods 0.000 description 1
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- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
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- 238000003384 imaging method Methods 0.000 description 1
- 230000004054 inflammatory process Effects 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
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- 238000010837 poor prognosis Methods 0.000 description 1
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- 230000002265 prevention Effects 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
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- 239000013589 supplement Substances 0.000 description 1
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- 238000010863 targeted diagnosis Methods 0.000 description 1
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
- G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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Abstract
The invention relates to an early warning system, which comprises an early warning rule configuration module, a data acquisition module, an early warning rule judgment module and an early warning output module; the early warning rule configuration module is used for configuring early warning rules, and a doctor configures early warning names, early warning ranges and corresponding suggestions through the early warning rule configuration module; the early warning rules comprise threshold early warning, difference early warning, trend early warning and weighting early warning; the data acquisition module is used for acquiring and uploading symptom and sign data of the patient; the early warning rules are stored in a database; the early warning rule judgment module is used for reading the early warning rules stored in the database, judging whether the early warning rules of threshold early warning, difference early warning, trend early warning and weighted early warning are met or not according to the comparison result of the symptom sign data of the patient and the early warning rules, which are acquired and uploaded by the data acquisition module, and if the early warning rules are met, pushing the early warning by the early warning output module; and if not, not pushing the early warning.
Description
Technical Field
The invention relates to the technical field of medical services, in particular to an early warning system.
Background
At present, the clinical diagnosis is mainly studied by paying attention to the auxiliary examination such as assay indexes and imaging, but the study of the disease history and symptom characteristics in the early stage of the onset is less, and the main reason of the diagnosis difficulty is that the symptoms are atypical, and particularly in the early stage of the onset, the onset is usually only fever, and mild local swelling and pain are accompanied. Some symptoms only have local mild pain and swelling, and the evaluation of the symptom indexes is generally qualitative rather than quantitative, some symptoms have slow change, are difficult to distinguish from the inflammation reaction process with normal injury only from subjective feeling or cannot draw enough attention to cause diagnosis difficulty, and also have the relative delay of targeted diagnosis and treatment measures caused by insufficient self-cognition of patients, so that the diagnosis and treatment are delayed, the functions of the patients are influenced, the mobility is reduced, the risk of adhesion is increased, the joint degeneration and the early occurrence of osteoarthropathy are caused, and the movement function of the patients is further influenced; serious patients have to require amputation treatment and even the life of the patient is threatened; some long-term clinical study follow-up also confirmed that the reasons for poor prognosis after patient complications were joint damage and discontinuation of rehabilitation therapy, and the main reasons for subjective dissatisfaction of patients were joint pain, stiffness and weakness. Therefore, the rehabilitation and prevention of various complications after joint injury are the difficulty and hot point of the current clinical treatment.
The literature indicates that a 'delay period' exists between the diagnosis of postoperative complications and the occurrence of clinical manifestations, clinical diagnosis delay mainly occurs in an out-of-hospital stage, and at present, normal data distribution of various symptoms is lack of a large amount of data, and the data are mostly general text descriptions or section follow-up data with a single index, and a more detailed change trend, and quantitative changes of various indexes are lack of sufficient data, so that reliable early warning cannot be provided for sick people.
Although the complication incidence is low, the harm is large, and the patient cannot see a doctor in time due to the lack of early recognition capability in the clinical practice at present, so that the delayed diagnosis period exists outside the hospital. The longer the delay diagnosis and treatment time, the greater the joint destruction. Therefore, a remote early warning model is established, double-phase early warning is carried out on the patient and the doctor, intelligent monitoring follow-up visit is realized, the in-time diagnosis rate is improved, the diagnosis delay period is shortened, and the joint injury is reduced.
In the prior art, although the quantitative collection of the symptomatology indexes is concerned, the quantitative collection of the symptomatology indexes is a single index, the influence of events and time change is not considered, the early warning is inaccurate, and the application range is small. Therefore, the most effective symptomatic indexes are required to be integrated, the time change trend is combined, various data processing modes are utilized, the existing data information is mined to the maximum extent, and the most effective early warning indexes are simplified, so that the model is established, the accuracy, the sensitivity, the stability, the applicability and the strain of the early warning model are improved, the dynamic monitoring is realized, and the method is suitable for popularization and is closer to clinical practice.
Disclosure of Invention
The invention aims to provide an early warning system, and the technical problems to be solved at least comprise how to assist diagnosis according to quantitative medical history, symptom and physical sign data changes, establish an early warning model, give prompt suggestions by mild people, enable patients to arrive at a hospital for reexamination as soon as possible, shorten a diagnosis delay period, particularly an out-of-hospital delay period, carry out early intervention treatment and reduce the possibility of injury to the lowest.
In order to achieve the above object, the present invention provides an early warning system, which includes an early warning rule configuration module, a data acquisition module, an early warning rule judgment module, and an early warning output module; the early warning rule configuration module is used for configuring early warning rules, and a doctor configures early warning names, early warning ranges and corresponding rehabilitation suggestions through the early warning rule configuration module; the early warning rules comprise threshold early warning, difference early warning, trend early warning and weighting early warning; the data acquisition module is used for acquiring and uploading symptom and sign data of the patient; the early warning rules are stored in a database; the early warning rule judging module is used for reading early warning rules stored in a database, judging whether the early warning rules of threshold early warning, difference early warning, trend early warning and weighted early warning are met or not according to the comparison result of the symptom sign data of the user and the early warning rules, which are acquired and uploaded by the data acquisition module, and if the early warning rules are met, pushing the early warning by the early warning output module; and if not, not pushing the early warning.
The threshold early warning refers to the early warning of a single acquisition item value; the difference early warning refers to the early warning of the difference of different index measurement values at the same time, and an absolute value is taken; the trend early warning refers to the early warning that a certain numerical value (such as an acquisition item, a difference value or a weighted value) is in ascending or descending trend change after multiple measurements or calculations for multiple days; the weighted early warning refers to comprehensive early warning which is carried out after data of all the acquisition items are added and calculated according to configured weights in a certain day.
The symptom and sign data of the patient comprise body temperature data, pain data, circumference data, angle data and skin temperature data, and disease or occurrence events and occurrence time.
The early warning rule of the body temperature data is as follows: pushing a threshold value for early warning when the temperature data submitted each time is less than 36 ℃ or more than 37.5 ℃; the method comprises the steps of submitting body temperature data within a certain time period (for example, continuously for 3 days, specifically adjustable, and configurable according to conditions) by taking the most value or mean value data of each day as a standard, and pushing a trend early warning if the rising trend exceeds 1.5 ℃.
The early warning rule of the pain data is as follows: the data items of the pain data are divided into time and parts, and if the pain value is larger than 5 when the pain data is submitted every time, threshold early warning is pushed; when the pain data is submitted, the most value or mean value data of each day is taken as a standard, the pain data is submitted within a certain time period (for example, 3 continuous days), and if the rising trend is more than 3, the trend early warning is pushed.
The early warning rule of the surrounding degree data is as follows: the data items of the circumference data are divided into the joint midpoint and the muscle abdominal circumference around the joint, and if the difference data value of the left and right circumferences is larger than 1cm when the circumference data is submitted every time, the difference early warning is pushed; and submitting the circumference data within a certain time period (for example, continuously for 3 days) by taking the maximum value or the mean value data of each day as a standard, and pushing a trend early warning if the value of the affected side or the difference value of the healthy side is in an ascending trend or a descending trend which is larger than 1 cm.
The early warning rule of the angle data is as follows: the angle data have different directions such as inward and outward rotation in flexion and extension in different joints and the like, and all comprise active and passive angles, actual and target angles and time for reaching the actual and target angles, and when the angle data are submitted every time, if the data value of the active and passive extension angle is greater than 0 degrees, threshold value early warning is pushed; and submitting the angle data within a certain time period (for example, continuously for 3 days) by taking the maximum or average data of each day as a standard, and pushing a trend early warning if the ascending trend is more than 5 degrees. And if the difference between the actual angle and the target angle is greater than 5 degrees or the difference between the active angle and the passive angle is greater than 20 degrees, pushing a difference value for early warning.
The early warning rule of the skin temperature data is as follows: the data items of the skin temperature data are the surface temperatures of all joints and the surrounding muscle abdomens, and when the skin temperature data are submitted every time, if the skin temperature data values are less than 30 ℃ or more than 35 ℃, threshold early warning is pushed; when skin temperature data are submitted, if the difference value of the skin temperatures on two sides is more than 2 ℃, pushing the difference value for early warning; and submitting the skin temperature data within a certain time period (for example, continuously for 3 days) by taking the maximum value or average value data of each day as a standard, and pushing a trend early warning if the skin temperature data rises by more than 2 ℃ when the skin temperature data is submitted.
The weighted early warning is that weights are respectively configured for 5 items of data of body temperature data, pain data, circumference data, angle data and skin temperature data and derivative calculation values (such as a circumference difference value, an active and passive angle difference value, an actual and target angle difference value and the like), the submitted data are calculated according to the weights, and if the submitted data accord with weighted early warning rules, weighted early warning is pushed; and if not, not pushing the weighted early warning.
In a preferred embodiment, the early warning output module pushes the early warning through a WeChat service number.
Advantageous effects
Compared with the prior art, the invention has the beneficial effects that: the early warning system comprehensively collects and analyzes the symptom and sign of the patient, improves the data processing mode, performs early warning judgment by combining the time change rule, improves the early warning accuracy, and provides a basis for post-operation remote intelligent follow-up visit. The symptomatology indexes related to the early warning system not only can be used for judging a complication early warning model, but also are important evaluation indexes of therapists in the rehabilitation process, and can be combined with activity equivalent indexes to establish an individualized diagnosis and treatment model, monitor the rehabilitation progress of patients, prompt and assist guidance for postoperative rehabilitation, supplement a supply side service mode, reduce the labor cost and solve the supply and demand contradiction. The comprehensive symptomatology indexes collected by the early warning system provide a large number of high-density values, data bases and reference values of structured identification for future rehabilitation quantitative evaluation, and also provide a theoretical basis for research and development of wearable equipment in future and realization of remote intelligent joint monitoring and rehabilitation.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a schematic structural diagram of the early warning system according to the present invention.
Fig. 2 is a schematic diagram of an early warning process of the early warning system according to the present invention.
Detailed Description
The present invention is described in more detail below to facilitate an understanding of the present invention.
As shown in fig. 1 and fig. 2, the early warning system of the present invention includes an early warning rule configuration module, a data acquisition module, an early warning rule determination module, and an early warning output module; the early warning rule configuration module is used for configuring early warning rules, and a doctor configures early warning names, early warning ranges and corresponding rehabilitation suggestions through the early warning rule configuration module; the early warning rules comprise threshold early warning, difference early warning, trend early warning and weighting early warning; the data acquisition module is used for acquiring and uploading symptom and sign data of the patient; the early warning rules are stored in a database; the early warning rule judging module is used for reading early warning rules stored in a database, judging whether the early warning rules of threshold early warning, difference early warning, trend early warning and weighted early warning are met or not according to the comparison result of the symptom sign data of the patient and the early warning rules, which are acquired and uploaded by the data acquisition module, and if the early warning rules are met, pushing the early warning by the early warning output module; and if not, not pushing the early warning.
The threshold early warning refers to the early warning of a single acquisition item value; the difference early warning refers to the early warning of the difference of the measured values on the left side and the right side, or the absolute value of the difference of the active angle and the passive angle; the trend early warning refers to the early warning that a certain acquisition item shows ascending or descending trend change after multiple measurements; the weighted early warning refers to the comprehensive early warning which is carried out after the data of all the acquisition items are added and calculated according to the configured weight in a certain day.
The symptom and sign data of the patient comprise body temperature data, pain data, circumference data, angle data and skin temperature data.
The early warning rule of the body temperature data is as follows: pushing a threshold value for early warning when the temperature data submitted each time is less than 36 ℃ or more than 37.5 ℃; and (3) submitting body temperature data continuously for 3 days by taking the minimum data of each day as a standard, and pushing a trend early warning if the rising trend exceeds 2 ℃.
The early warning rule of the pain data is as follows: the data items of the pain data are divided into patella midpoint and thigh circumference, and if the pain value is greater than 6 every time the pain data is submitted, threshold early warning is pushed; when the pain data are submitted, if the pain difference value of the left side and the right side is larger than 3, pushing a difference value early warning; and submitting pain data for 3 consecutive days by taking the lowest data of each day as a standard, and pushing a trend early warning if the difference value of two sides is more than 3.
The early warning rule of the surrounding degree data is as follows: dividing data items of the circumference data into patellar midpoint and thigh circumference, and pushing threshold value early warning if the circumference data value is less than 3 when the circumference data is submitted each time; and (3) submitting the circumference data for 3 consecutive days by taking the lowest data of each day as a standard, and pushing a trend early warning if the difference values on the two sides are in an ascending trend or a descending trend and reach a preset numerical range.
The early warning rule of the angle data (the angle is an affected part photo uploaded by a patient through photographing, 3 points are drawn, and the formed angle) is as follows: the data items of the angle data are divided into active and passive data and flexion and extension data, and when the angle data are submitted every time, if the extension angle data value is larger than 3 degrees or the flexion angle is smaller than 90 degrees, threshold early warning is pushed; and taking the minimum data of each day as a standard, continuously submitting angle data for 3 days, and pushing a trend early warning if the downward trend of the buckling angle is greater than 10 degrees.
The early warning rule of the skin temperature data is as follows: the data items of the skin temperature data are divided into the patellar midpoint and thigh circumference, and when the skin temperature data are submitted each time, if the skin temperature data value is less than 30 ℃ or more than 35 ℃, threshold early warning is pushed; when skin temperature data are submitted, if the difference value between the left skin temperature and the right skin temperature is larger than 4 ℃, pushing a difference value for early warning; and submitting the skin temperature data for 3 consecutive days by taking the lowest data of each day as a standard, and pushing a trend early warning if the skin temperature data rises by more than 4 ℃ when the skin temperature data is submitted.
The weighted early warning is that weights are respectively configured on 5 data of body temperature data, pain data, circumference data, angle data and skin temperature data, the submitted data is calculated according to the weights, and if the weighted early warning accords with weighted early warning rules, weighted early warning is pushed; and if not, not pushing the weighted early warning.
In a preferred embodiment, the warning output module pushes the warning through a patient terminal (e.g., a WeChat service number).
The configuration method of the threshold early warning comprises the following steps:
selecting a group, selecting an acquisition item, selecting a secondary acquisition association item, setting an early warning range, configuring early warning content and a rehabilitation suggestion.
The configuration method of the difference value early warning comprises the following steps:
selecting a group, selecting an acquisition item, selecting a secondary acquisition association item, setting an early warning range, configuring early warning content and a rehabilitation suggestion.
The configuration method of the trend early warning comprises the following steps:
selecting a group, selecting an acquisition item, selecting a secondary acquisition association item, selecting a data standard (highest value/lowest value/average value), selecting a trend (ascending/descending), selecting comparison days, setting an early warning range, configuring early warning content and a rehabilitation suggestion.
The configuration method of the weighted early warning comprises the following steps:
selecting a group, selecting an acquisition item, configuring the weight of the acquisition item, selecting a data standard (highest value/lowest value/average value), setting an early warning range, configuring early warning content and a rehabilitation suggestion.
In the early warning system, roles are divided into a patient (user) and a doctor (rehabilitation). The permissions are divided into: when the early warning rules are configured, the groups need to be selected, and the users in the selected groups can only have the early warning push service.
The foregoing describes preferred embodiments of the present invention, but is not intended to limit the invention thereto. Modifications and variations of the embodiments disclosed herein may be made by those skilled in the art without departing from the scope and spirit of the invention.
Claims (8)
1. The early warning system is characterized by comprising an early warning rule configuration module, a data acquisition module, an early warning rule judgment module and an early warning output module; the early warning rule configuration module is used for configuring early warning rules, and a doctor configures early warning names, early warning ranges and corresponding suggestions through the early warning rule configuration module; the early warning rules comprise threshold early warning, difference early warning, trend early warning and weighting early warning; the data acquisition module is used for acquiring and uploading symptom and sign data of the patient; the early warning rules are stored in a database; the early warning rule judging module is used for reading early warning rules stored in a database, judging whether the early warning rules of threshold early warning, difference early warning, trend early warning and weighted early warning are met or not according to the comparison result of the symptom sign data of the patient and the early warning rules, which are acquired and uploaded by the data acquisition module, and if the early warning rules are met, pushing the early warning by the early warning output module; and if not, not pushing the early warning.
2. The warning system of claim 1, wherein the threshold warning is a warning of a single acquisition item value; the difference early warning refers to the early warning of the difference of different measured values in the same time, and an absolute value is taken; the trend early warning refers to the early warning that a certain numerical value is changed in an ascending or descending trend after being measured or calculated for multiple times; the weighted early warning refers to the comprehensive early warning performed after the data of all the acquisition items are added and calculated according to the configured weights.
3. The warning system of claim 1 wherein the patient symptom sign data includes body temperature data, skin temperature data, pain data, circumference data, angle data, disease or occurrence and time of occurrence.
4. The early warning system of claim 3, wherein the data acquisition module is capable of acquiring one or more arbitrary indicators, and when an early warning occurs, the data acquisition module is capable of requesting acquisition of more indicators, raising acquisition frequency or time requirements, and starting more early warning judgments.
5. The early warning system of claim 3, wherein the weighted early warning is to perform different combinations of two or more of body temperature data, pain data, circumference data, angle data and skin temperature data, configure weights respectively, calculate the submitted data according to the weights, and push the weighted early warning if the submitted data meets weighted early warning rules; and if not, not pushing the weighted early warning.
6. The early warning system of claim 1 or 3, wherein the early warning is divided into different grades, and the acquisition requirements and suggestions corresponding to the different grades can be configured and adjusted differently in the early warning rule configuration module.
7. The warning system of claim 1, wherein the warning output module pushes the warning through a patient terminal.
8. The warning system of claim 1, wherein the doctor end is capable of screening out different types of warnings.
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| CN116192605A (en) * | 2022-12-30 | 2023-05-30 | 北京云建信科技有限公司 | Rule engine system and method for intelligent construction site |
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